Privacy-Preserving Technologies in Data
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This study develops and evaluates a context-aware Hybrid Local Differential Privacy–Federated Learning (LDP-FL) approach for protecting digital footprints while retaining useful machine-learning performance.
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Digital services increasingly depend on information generated from users’ online activities, yet the same traces that make these services useful can expose individuals to re-identification and inference. This study develops and evaluates a context-aware Hybrid Local Differential Privacy–Federated Learning (LDP-FL) approach for protecting digital footprints while retaining useful machine-learning performance. The work distinguishes digital footprints into active, passive and hybrid categories and assigns privacy protection according to feature sensitivity rather than applying a single noise level to every attribute. The proposed pipeline perturbs sensitive features locally with a Laplace mechanism, clips local gradients, and uses Federated Averaging to train a global model without centralising raw client data.
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@article{Sophia2026Context,
title = {A Context-Aware Hybrid Local Differential Privacy–Federated Learning Framework for Digital Footprint Protection},
author = {Onyia Ogochukwu Sophia and Akawuku Mirian Ogheneyovwino and Chekwube Georgina Nwankwo},
journal = {International Journal of Innovative Science and Research Technology (IJISRT)},
year = {2026},
doi = {10.38124/ijisrt/26sep797},
url = {https://doi.org/10.38124/ijisrt/26sep797}
}FAQ
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